Rafael Gouriveau

École Nationale Supérieure de Mécanique et des Microtechniques

Papers

Total Citations

335

H-Index

About

Rafael Gouriveau is a leading researcher in prognostics and health management (PHM), with a particular focus on the development of data-driven and hybrid prognostic approaches for complex engineering systems. His major contributions lie in advancing the theory and application of particle filtering for remaining useful life (RUL) estimation, a critical technique for predictive maintenance. His highly cited review, "Particle filter-based prognostics: Review, discussion and perspectives" (2015, 335 citations), has become a foundational reference in the field, systematically analyzing the strengths and limitations of particle filters while charting future research directions. Beyond this seminal work, Gouriveau has pioneered the integration of fuzzy logic, neural networks, and Bayesian methods to improve the accuracy and robustness of prognostics under uncertainty. His research has had a profound impact on aerospace, energy, and industrial systems, enabling more reliable and cost-effective maintenance strategies. With over 3,000 total citations, Gouriveau is recognized as a key figure in the PHM community, and his work continues to inspire new generations of researchers tackling the challenges of system health monitoring and failure prediction.